m1_huggingface_diffusers_demo
nb_conda_kernels
| m1_huggingface_diffusers_demo | nb_conda_kernels | |
|---|---|---|
| 5 | 3 | |
| 15 | 564 | |
| - | 0.9% | |
| 10.0 | 8.3 | |
| over 1 year ago | 6 days ago | |
| Jupyter Notebook | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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m1_huggingface_diffusers_demo
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JupyterLab 4.0
The trick is that you have to deactivate the virtual environment and then resource it after adding Jupyter to that virtual environment.
Most shells cache executable paths, so the path for jupyter will be the global path, not the one for your virtual environment. This is unfortunately not at all obvious and leads to very hard to track down bugs that seem to disappear and reappear if you aren't familiar with the issue.
I have a recipe here which always works: https://github.com/nlothian/m1_huggingface_diffusers_demo#se...
If you don't have requirements.txt then do this: `pip3 install jupyter` for that line, then `deactivate` and `source ./venv/bin/activate`.
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Bunny AI
This is how I did it on an M1 in September: https://github.com/nlothian/m1_huggingface_diffusers_demo
I think it probably needs updating now, but it should give you something to start with.
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One-Click Install Stable Diffusion GUI App for M1 Mac. No Dependencies Needed
On my M1 MAx with 32 GB I'm getting 1.5 iterations/second (ie, ~30 seconds for the standard 50 iterations) using this example: https://github.com/nlothian/m1_huggingface_diffusers_demo
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Nvidia Hopper Sweeps AI Inference Benchmarks in MLPerf Debut
Out of interest I've been running a bunch of the huggingface version of StableDiffusion using the M1 accelerated branch on my M1 Max[1]. I'm getting 1.54 it/s compared to 2.0 it/s for a Nvidia T4 Tesla on Google Collab.
T4 Tesla gets 21,691 queries/second for for ResNet, compared to 81,292 q/s for the new H100, 41,893 q/s for the A100 and 6164 q/s for the new Jetson.
So you can expect maybe 15,000 q/s on a M1 Max. But some tests seem to indicate a lot less[2] - not sure what is happening there.
[1] Setup like this: https://github.com/nlothian/m1_huggingface_diffusers_demo
[2] https://tlkh.dev/benchmarking-the-apple-m1-max#heading-resne...
nb_conda_kernels
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JupyterLab 4.0
If you use conda there are extensions that can help with this by automatically registering any available conda environments that include ipykernel in your Jupyter Lab environment.
nb_conda_kernels is pretty reliable but not actively maintained. Gator from the mamba folks is new and still a bit rough around the edges but looks like it will be pretty slick eventually.
https://github.com/Anaconda-Platform/nb_conda_kernels
https://github.com/mamba-org/gator
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How to open Notebook in new tab from a Launcher Terminal
it sounds like what you're trying to do is what https://github.com/Anaconda-Platform/nb_conda_kernels does, except with pipenv. I unfortunately do not have a solution for you for pipenv, but nb_conda_kernels does seamlessly allow you to launch kernels in environments other than the environment Jupyter is running in without having to start multiple instances of jupyter.
What are some alternatives?
ai-notes - notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under the /Resources folder.
JupyterLab - JupyterLab computational environment.
diffusionbee-stable-diffusion-ui - Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
papermill - 📚 Parameterize, execute, and analyze notebooks
sd-buddy - Companion desktop app for the self-hosted M1 Mac version of Stable Diffusion
gator - Conda environment and package management extension from within Jupyter
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
notebooker - Productionise & schedule your Jupyter Notebooks as easily as you wrote them.
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
cufflinks - Productivity Tools for Plotly + Pandas
stable-diffusion - A latent text-to-image diffusion model
datasette - An open source multi-tool for exploring and publishing data